Motivation and aim
The energy transition leads to increasing electricity transmission grid planning like:
1. Network Development Plan
2. Cost Benefit Analysis
3. Bidding Zone Review
Thereto necessary electricity market simulations require today installations, maintenance, schooling and manual data processing. Furthermore, only data research and calibration simulations create a usable input data set with close to reality results.
Maon developed an electricity market simulation and provides it as a browser-based service. This enables simulations without the installation of software or hardware. Moreover, calibrated input data for Europe-wide and yearly simulations are available.
This online platform involves neither command lines nor direct application of optimization software. Instead, users are supported by parameterization tool kits and data quality checks. Additionally, unit commitment and market price results are prepared automatically for evaluations and applications like social welfare analysis or power-flow simulations.
Access to simulations, data and processing is available via browser for immediate usage and via web service for embedding machine-to-machine processes. Figure 1 depicts the browser-based graphical user interface and typical workflow steps.
Figure 1: Service and workflow
The graphical user interface provides features for up- and download, parameterization, data visualization and the simulation. Due to thethe platform can be used via tablet or smart phone. Further, the microservice software architecture enables high-performance simulations on demand in a timely fashion (software-as-a-service).
The electricity market simulation handles Europe-wide data sets with degrees of freedom in international exchange, demand, renewables, thermal and hydro power plants for a time coupled year with a hourly granularity. It is based on a three-staged optimization problem solving procedure displayed in figure 2.
Figure 2: Electricity market simulation
The first and third step handle yearly couplings like seasonal hydro basin filling levels. The combinatorial complexity is managed by a stepwise optimization in the second step. So integer restrictions like minimum power or minimum up time can be considered. All simulations run in the background at a high-performance computing cluster.
With a positive initial plausibility, feasibility and complexity check (takes two minutes), this procedure guarantees a simulation run without abort. In case of failure, the user gets a problem description and a solution proposal. Hence, manual data processing is reduced to a minimum.
Pre-parametrized data sets comprise historical years and typical scenarios for example from the Ten-Year Network Development Plan. 2018 was our first parametrized scenario including the Flow-Based Market Coupling domain (approximately 458,000 restrictions), thermal power plants and coupled hydro power plant networks (approximately 5,000 units). Figure 3 displays resulting generation amounts and compares them with historical observations.
Figure 3: Exemplary total electricity generation amounts per bidding zone
Generation amounts get close to historical values. Thus, European-wide, annual and high resolution unit commitment and prices can be derived. Based on Maon, in-depth and close to reality planning can be carried out. Therefore, raw results are automatically processed for social welfare analysis or power-flow simulations in PSS/E (Siemens) or Integral (FGH).
Maon enables web-based and rapid electricity market simulations with minimal effort and without the need for permanent provision of software and hardware.